Search Results (764)

Lubrication is a critical aspect of the metal forming process and it is strongly influenced by the surface texture of the tool-forming surfaces. This study is focused on determining the effect of surface finish and heat treatment on wettability involving commonly used lubrication agents. Three different finishing states are evaluated (as-ground, as-polished and as-nitrided). Surface topography was measured using a focus variation microscope. Parametric evaluation was carried out according to ISO 25178, including fractal methods. The functional relations between the finish state and wettability, lubricating agent and wettability, selected surface parameters and wettability, as well as between finish state and selected surface parameters, were designated. The results showed that surface finishing treatments, particularly nitriding, influenced both surface roughness and wettability, with varying effects observed across different lubricants and droplet sizes. The findings provide valuable insights into the optimization of lubrication strategies for metal forming processes, highlighting the importance of tailored surface treatments for enhanced tool performance and longevity. Full article

Vegetation is a fundamental component of terrestrial ecosystems, and accurately assessing the effects of seasonal climate variations, extreme weather events, and land use changes on vegetation dynamics is crucial. The Ganjiang River Basin (GRB), a key region for water conservation and recharge in southeastern China, has experienced significant land use changes and variable climate in the past. However, comprehensive evaluations of how these changes have impacted vegetation remain limited. To address this gap, we used machine learning models (random forest and XGBoost) to assess the impact of seasonal and extreme climate variables, land cover, topography, soil properties, atmospheric CO₂, and night-time light intensity on vegetation dynamics. We found that the annual mean NDVI showed a slight increase from 1990 to 1999 but has decreased significantly over the last 8 years. XGBoost was better than the RF model in simulating the NDVI when using all five types of data source (R² = 0.85; RMSE = 0.04). The most critical factors influencing the NDVI were forest and cropland ratio, followed by soil organic carbon content, elevation, cation exchange capacity, night-time light intensity, and CO₂ concentration. Spring minimum temperature was the most important seasonal climate variable. Both linear and nonlinear relationships were identified between these variables and the NDVI, with most variables exhibiting threshold effects. These findings underscore the need to develop and implement effective land management strategies to enhance vegetation health and promote ecological balance in the region. Full article

NO₂ primarily originates from natural and anthropogenic emissions. Given China’s vast territory and significant differences in topography and meteorological conditions, a detailed understanding of the impacts of weather and human emissions in different regions is essential. This study employs Kolmogorov–Zurbenko (KZ) filtering and stepwise multiple linear regression to isolate the effects of meteorological conditions on tropospheric NO₂ vertical column densities. Long term trends indicate an overall decline, with anthropogenic contribution rates exceeding 90% in Shanghai, Changchun, Urumqi, Shijiazhuang, and Wuhan, where interannual variations are primarily driven by human emissions. In Guangzhou, the anthropogenic contribution rate exceeds 100%, highlighting the significant impact of human factors in this region, although meteorological conditions somewhat mitigate their effect on NO₂. In Chengdu, meteorological factors also play a role. Seasonal variations display a U-shaped trend, and there are significant differences in the impact of meteorological factors on seasonal variations among different regions. Meteorological contribution rates in Changchun and Chengdu are below 36.90% and anthropogenic contributions exceed 63.10%. This indicates that changes in NO₂ are less influenced by meteorological factors than by human activities, with human emissions dominating. In other regions, meteorological contributions are greater than those from human activities. Full article

The Loess Plateau (LP) in China is an ecologically fragile region that has long faced challenges such as soil erosion, water shortages, and land degradation. The spatial and temporal variations in ecological environment quality on the LP from 2000 to 2020 were analyzed using the Remote Sensing Ecological Index (RSEI) on the Google Earth Engine (GEE) platform. The Sen, Mann–Kendall, and Hurst exponent analyses were used to examine the spatial variation trends over the past 20 years, while Geodetector identified key factors influencing RSEI changes and their interactions. The results indicate that (1) RSEI effectively represents the ecological and environmental quality of the LP, with 47% of the study area’s annual mean RSEI values over the 20-year period classified as moderate, ranging from 0.017 to 0.815. (2) Ecological quality trends showed improvement in 72% of the area, with a 90% overall increase, but 84% of these trends are not likely to continue. (3) Key factors influencing RSEI changes during abrupt change years included precipitation, land use/land cover, and soil sediment content, with precipitation and topography emerging as primary influences on ecological quality. Although natural factors largely drive ecological changes, human activities also exert both positive and negative effects. This study underscores the importance of sustainable ecological management and provides policy insights for advancing ecological civilization on the LP, contributing to the achievement of the Sustainable Development Goals (SDGs). Full article

The accuracy of the Weather Research and Forecasting (WRF) model’s predictions for air temperature and precipitation in northwestern Greece varies under different weather conditions. However, there is a lack of understanding regarding how well the model performs for specific Weather Types (WTs), especially in regions with a complex topography like NW Greece. This study evaluates the WRF model’s ability to predict 2 m air temperature and precipitation for 10 objectively defined WTs. Forecasts are validated against observations from the station network of the National Observatory of Athens, focusing on biases and skill variation across WTs. The results indicate that anticyclonic WTs lead to a significant overestimation of early morning air temperatures, especially for inland stations. The precipitation forecast skill varies depending on the threshold and characteristics of each WT, showing optimal results for WTs where precipitation is associated with a combination of depression activity, and orographic effects. These findings indicate the need for adjustments based on WT in operational forecasting systems for regions with similar topographical complexities. Full article

Background/Objectives: Lebanon, one of the oldest centers of grapevine (Vitis vinifera L.) cultivation, is home to a rich diversity of local grape varieties. This biodiversity is linked to the country’s unique topography and millennia of cultural history. However, the wine industry primarily utilizes international varieties, putting many local varieties at risk of extinction. Methods: In this study, we analyzed 202 samples from old vineyards, home gardens, and private collections using 21 microsatellite markers to assess their identity and genetic diversity. Results: A total of 67 different genotypes were identified, with 34 not matching any existing profiles in the consulted databases, based on comparisons with the European Vitis Database, the Vitis International Variety Catalogue (VIVC), and the databases established in two previous studies conducted in Armenia and Lebanon. Cluster analyses revealed Lebanon’s rich diversity of local grape varieties, highlighting cases of synonymy, homonymy, and misnaming. All loci were polymorphic, with 228 alleles and an average of 11.4 alleles being detected. The highest number of alleles was observed at the VVIV67 locus (19 alleles), while the lowest was found at the VVIQ52 and VVIN73 loci (5 alleles). The observed heterozygosity was 0.732, slightly below the expected value of 0.757, with gene diversity varying among the markers. Conclusions: Of the 67 genetic profiles identified, 34 are absent from national and international databases, underscoring Lebanon as a hotspot for grapevine genetic diversity. This unique genetic variation, which includes several synonyms due to geographic isolation, could provide valuable opportunities for producing distinctive wines and emphasizes the need for further research and documentation. Full article

Accurate soil total phosphorus (TP) prediction is essential to support sustainable agricultural practices and formulate ecological conservation protection policies, particularly in complex karst landscapes with high spatial variability and high phosphorus and cadmium content and interactions, complicating nutrient management. This study uses GIS and geostatistical methods to analyze the spatial distribution, influencing factors, and predictive modeling of soil TP in the karst region of northern Mashan County, Guangxi, China. Using 427 surface soil samples, we developed five predictive models: ordinary kriging (OK), regression kriging (RK) and geographically weighted regression kriging (GWRK) combined with environmental variables such as land uses, soil types, and topographic factors; residual mean-centered kriging (MM_OK), and residual median-centered kriging (MC_OK). Our results indicate that higher TP levels were observed in agricultural lands (paddy fields and dry land, at 766 and 913 mg·kg⁻¹, respectively) may due to fertilization, while forests and shrublands showed lower TP levels (383 and 686 mg·kg⁻¹, respectively), reflecting natural phosphorus cycling. The high-value areas of soil TP concentration are in the karst areas in the west and east of the study area, and the low-value area is in the Hongshui River valley in the north of Mashan. The spatial distribution of soil TP is affected by land use, soil type, and topography. The GWRK model exhibited superior accuracy (80.6%), with predicted concentration of TP closely aligning with observed TP values, effectively capturing fine spatial variations, and showing the lowest mean standardized error, average standard error, and mean absolute error. GWRK also achieved the highest R² (0.67), demonstrating robust predictive capability. MM_OK and MC_OK models performed well and showed smoother spatial transitions, while the OK model displayed the lowest predictive accuracy (62%). By utilizing spatially adaptive weighting, GWRK and its residual-centered kriging method improve soil TP’s prediction accuracy and smoothness in karst areas, providing a reference for targeted soil conservation and sustainable agricultural practices in spatially complex karst environments. Full article

Soil available nutrients (SANs) can be rapidly converted, absorbed, and utilized by crops. The study of the spatial distribution and variation of SANs, as well as their response to environmental factors, is crucial for precision fertilization and soil ecosystem function regulation. In this study, 220 soil surface-layer samples (0–20 cm) were collected in 2019 from loquat orchards in the mid-low mountain hilly areas of central China to explore the spatial distribution and variation of SANs, as well as the effects of environmental factors (including the topography, vegetation index, soil property, and climate) on SANs, using a path analysis model. The results showed that the available potassium (AK) and ammonium nitrogen (AN) levels exhibited a moderate average content, which was 123.14 mg·kg⁻¹ and 119.03 mg·kg⁻¹, respectively, whereas available phosphorus (AP) levels displayed a high average content (26.78 mg·kg⁻¹), and all three SANs showed an uneven spatial distributions. The nugget effect values of AK and AN ranged from 25% to 75%, indicating moderate spatial variation, and those of AP were <25%, suggesting high spatial variation. Furthermore, the mean annual precipitation (MAP) had a direct positive effect on AK levels, while slope had an indirect effect on AK levels through the ratio vegetation index (RVI), suggesting that precipitation had greater impact on AK levels than topography. Soil erosion had a direct negative effect on AP and AN levels, accelerating the loss of SANs. The MAP and soil type (ST) directly affected soil AN content. Specifically, sufficient precipitation and fine soil facilitated the storage and conversion of AN in soil. Taken together, our path analysis indicated that all the four categories of environmental factors had direct or indirect effects on SANs, and our geostatistical analysis revealed the spatial distribution and variation law of SANs in the study area. Our findings offer a theoretical basis and valuable references for achieving precision fertilization in orchards and improving loquat yield and quality. Full article

The topographies of soil erosion and deposition are critical factors that significantly influence soil quality, subsequently impacting the erodibility of soils in karst regions. However, the investigation into the effects of erosion and deposition topographies on soil erodibility across different stages of vegetation succession in karst trough valleys is still at a preliminary stage. Therefore, three distinct topographic features (dip slopes, anti-dip slopes, and valley depressions) were selected at erosion (dip/anti-dip slope) and deposition sites (valley) to investigate the spatial heterogeneity of soil physicochemical properties, infiltration capacity, aggregate stability, and erodibility in karst trough valleys. Additionally, five different stages of vegetation succession in karst forests were considered: Abandoned land stage (ALS), Herb stage (HS), Herb-Shrub stage (HES), Shrub stage (SHS), and Forest stage (FS). Additionally, the relationships among these factors were analyzed to identify the key driving factors influencing soil erodibility. The results revealed that soil physicochemical properties and soil aggregate stability at the deposition site were significantly superior to those at the erosion site. The FS resulted in the best soil physicochemical properties, whereas the HS resulted in the highest soil aggregate stability within the deposition site. However, the soil infiltration capacity at the erosion site was significantly greater than that at the deposition sites. The ALS had the strongest soil infiltration capacity at both the erosion and deposition sites. The soil erodibility at erosion sites (0.064) was significantly greater than that at deposition sites (0.051), with the highest soil erodibility observed on anti-dip slopes during the HES at erosion sites (0.142). The structural equation model reveals that erosion and deposition topographies, vegetation succession, soil physicochemical properties, soil aggregates, and soil infiltration characteristics collectively account for 88% of the variation in soil erodibility under different conditions. Specifically, both direct and indirect influences on soil erodibility are most significantly exerted by soil aggregate stability and vegetation succession. This study provides scientific evidence to support the management of soil erosion and ecological restoration in karst trough valleys while offering technical assistance for regional ecological improvement and poverty alleviation. Full article

Amid persistent global food security challenges, the efficient utilization of cultivated land resources has become increasingly critical, as optimizing Cultivated Land Utilization Efficiency (CLUE) is paramount to ensuring food supply. This study introduced a cultivated land utilization index (CLUI) based on Fractional Vegetation Cover (FVC) to assess the spatiotemporal variations in Henan Province’s CLUE. The Theil–Sen slope and the Mann–Kendall test were used to analyze the spatiotemporal variations of CLUE in Henan Province from 2000 to 2020. Additionally, we used a genetic algorithm optimized Artificial Neural Network (ANN) and a particle swarm optimization-based Random Forest (RF) model to assess the comprehensive in-fluence between topography, climate, and human activities on CLUE, in which incorporating Shapley Additive Explanations (SHAP) values. The results reveal the following: (1) From 2000 to 2020, the CLUE in Henan province showed an overall upward trend, with strong spatial heterogeneity across various regions: the central and eastern areas generally showed decline, the northern region remained stable with slight increases, the western region saw significant growth, while the southern area exhibited complex fluctuations. (2) Natural and economic factors had notable impacts on CLUE in Henan province. Among these factors, population and economic factors played a dominant role, whereas average temperature exerted an inhibitory effect on CLUE in most parts of the province. (3) The influenced factors on CLUE varied spatially, with human activity impacts being more concentrated, while topographical and climatic influences were relatively dispersed. These findings provide a scientific basis for land management and agricultural policy formulation in major grain-producing areas, offering valuable insights into enhancing regional CLUE and promoting sustainable agricultural development. Full article

New observations of ocean surface topography obtained by wide-swath satellite interferometry require new capabilities to process spatially correlated errors in order to assimilate these data into numerical models. The sea surface height (SSH) variations have to be weighted against other types of assimilated data using information on their precision, as represented by the inverse of the SSH error covariance matrix R. The latter can be well approximated by a block-circulant (BC) structure and, therefore, allows numerically efficient implementation in operational data assimilation (DA) systems. In this note, we extend the technique of approximating R for wide-swath altimeters by including the uncertainties associated with the state of the atmosphere. It is shown that such an extension keeps the BC approximation error within acceptable (±10%) bounds in a wide range of environmental conditions and could be beneficial for improving the accuracy of SSH retrievals from wide-swath altimeter observations. Full article

This study focuses on the Kunming municipal area, located in the low-latitude plateau of southwestern China, utilizing remote sensing image data from four distinct periods between 2005 and 2020 to analyze changes in its green landscape patterns. Net primary productivity (NPP) was employed as a metric for carbon sequestration analysis to assess variations in NPP within the Kunming municipal area. Based on Pearson correlation analysis and the XGBoost-SHAP model, the correlations, important indicators, and responses of changes in the green space patterns of the Kunming municipal area to changes in carbon sequestration were analyzed and combined with policy and human factors. The findings indicate the following: (1) From 2005 to 2020, the area proportions of various green space types within the Kunming municipal area were ranked as follows: forest land > grassland > cultivated land > water bodies. (2) Between 2005 and 2015, the patch shapes of green spaces became increasingly complex, with heightened fragmentation among patches. After 2015, this complexity was reduced while connectivity continued to decline alongside an increase in the landscape heterogeneity and richness. (3) Over the period from 2005 to 2020, NPP values for cultivated land, forest land, and grassland exhibited a trend of decreasing and then increasing, reaching their lowest point in 2010. High NPP areas were predominantly found in regions characterized by a hilly topography, elevated altitudes, and substantial natural vegetation cover. (4) There was a significant correlation between green space pattern indices and NPP (p < 0.01), with SHDI, CONTAG, and DIVISION identified as three critical indices influencing NPP. The relationship between landscape patterns and carbon sequestration was most pronounced during the period from 2015 to 2020, followed by that from 2005 to 2010. Full article

Satellite-derived high-resolution soil moisture and precipitation data have become widely adopted in natural hazard and climate change research. Landslide susceptibility mapping, which often relies on static predisposing factors, faces challenges in accounting for temporal changes, limiting its efficacy in accurately identifying potential locations for landslide occurrences. A key challenge is the lack of sufficient ground-based monitoring networks for soil moisture and precipitation, especially in remote areas with limited access to rain gauge data. This study addresses these limitations by integrating static landslide conditioning factors—such as topography, geology, and landscape features—with high-resolution dynamic satellite data, including soil moisture and precipitation. Using machine learning techniques, particularly the random forest (RF) algorithm, the approach enables the generation of dynamic landslide susceptibility maps that incorporate both spatial and temporal variations. To validate the proposed method, two significant rainfall events that occurred in Italy in October and November 2019—each triggering more than 40 landslides—were analyzed. High-resolution satellite rainfall and soil moisture data were integrated with statistical conditioning factors to identify high-probability landslide areas successfully. A differential susceptibility map was generated for these events to compare the results between them, illustrating how susceptibility variations within the study area are influenced by hydrological factors. The distinct susceptibility patterns associated with different hydrological conditions were accurately captured. It is suggested that future research focus on leveraging time-series high-resolution satellite data to enhance landslide susceptibility assessments further. Full article

The performance of solar photovoltaic systems is impacted by dust accumulation, raising maintenance concerns and discouraging wider adoption to accelerate decarbonization pathways. This research investigates the influence of environmental dynamics on dust accumulation based on several locations, considering weather conditions, seasonality, and angular installation variations, over a three-month period. Low-iron glass coupons were employed to collect on-site soiling from four different locations: agricultural, residential, industrial, and desert. The samples collected were characterized using scanning electron microscopy (SEM) for morphology, X-ray diffraction (XRD) for mineralogy, energy-dispersive X-ray spectroscopy (EDX) for elemental analysis, spectrophotometry for optical properties, and I–V tracing for efficiency analysis. The data were processed using ANFIS techniques to extract the maximum power point (MPP) and reduce the power losses. The results showed significant differences in the dust properties across the sites, influenced by the topography, weather conditions, and human activity. The measurements revealed a decrease in transmittance of up to 17.98%, resulting in power losses of up to 22.66% after three months. The findings highlight the necessity for tailored maintenance strategies to mitigate the impact of human activities and site-specific factors on performance. This could be employed in developing predictive models providing valuable insights for sustaining solar energy systems. Full article

Density currents, fluid flows driven by differences in density, play a crucial role in disaster prevention for water pollution and tsunami mitigation, particularly due to thermal releases from power plants. Understanding their dynamics is pivotal for effective mitigation strategies. While the influence of seabed and lake bottom topography on density currents is well-studied, research on how embankment shapes affect these currents has been limited. This study aimed to fill this gap by experimentally and numerically analyzing the flow dynamics of density currents using various embankment shapes in a controlled water tank environment. The findings revealed distinct variations in density perturbation across different embankment shapes. Specifically, density currents exhibited reduced head velocities in embankments shaped as right-angled triangles, rectangles, and L-shapes, in that sequential order. This research underscores the significance of embankment design in modifying density currents, offering valuable insights for optimizing disaster management strategies related to water pollution and tsunami hazards induced by thermal effluents from industrial sources. Full article